The function_score query is the
ultimate tool for taking control of the scoring process. It allows you to
apply a function to each document that matches the main query in order to
alter or completely replace the original query _score.

In fact, you can apply different functions to subsets of the main result set by
using filters, which gives you the best of both worlds: efficient scoring with
cacheable filters.

It supports several predefined functions out of the box:

weight

Apply a simple boost to each document without the boost being
normalized: a weight of 2 results in 2 * _score.

field_value_factor

Use the value of a field in the document to alter the _score, such as
factoring in a popularity count or number of votes.

random_score

Use consistently random scoring to sort results differently for every user,
while maintaining the same sort order for a single user.

Decay functions—linear, exp, gauss

Incorporate sliding-scale values like publish_date, geo_location, or
price into the _score to prefer recently published documents, documents
near a latitude/longitude (lat/lon) point, or documents near a specified price point.

script_score

Use a custom script to take complete control of the scoring logic. If your
needs extend beyond those of the functions in this list, write a custom
script to implement the logic that you need.

Without the function_score query, we would not be able to combine the score
from a full-text query with a factor like recency. We would have to sort
either by _score or by date; the effect of one would obliterate the
effect of the other. This query allows you to blend the two together: to still
sort by full-text relevance, but giving extra weight to recently published
documents, or popular documents, or products that are near the user’s price
point. As you can imagine, a query that supports all of this can look fairly
complex. We’ll start with a simple use case and work our way up the
complexity ladder.